The University website has this link to the course.
I am excited by this development. Jocelyn will add a distinctive voice and approach to the sharing of insights into the analysis of performance.
I was fortunate to meet her during her undergraduate study at the University and then watched with admiration as she completed an Honours’ project in performance analysis, became a performance analysis scholar at the Australian Institute of Sport and received her PhD.
In addition to her research interests, Jocelyn has also explored the possibilities of creating open educational resources.
With her permission I would like to share some news of the graduate certificate.
I will be encouraging a Bring Your Own Software approach to the course, as I will be using Open Source software such as RStudio and LongoMatch. Students will have access to Tableau. I will also be using Excel quite a lot throughout the course.
I preparing a MOOC to run on the Canvas Network which will be a 4-week taster of the entire Graduate Certificate (one week for each unit). This will commence in January 2017.
I am delighted with the open aspects of the course. Jocelyn is discussing how her approach might fit in with Roland Goecke‘s work at the University of Canberra to offer a Masters in Data Science with a Sport Analytics strand.
This is the content of the Graduate Certificate course in Sports Analytics:
Unit 1: Performance Analysis in Sport
1.1 Identifying Performance Indicators
1.2 Designing Observational Systems and Collecting Data
1.3 Data Analysis and Interpretation
1.4 Feedback and Communication
- Collecting sports data
- Analysing data
- Visualising data
- Online quiz
- Match Analysis assessment
Unit 2: Athlete Monitoring
2.1 Player tracking
2.2 Monitoring athletes with self-report systems
2.3 Training load and injury
2.4 Performance testing
- Analysing player tracking data
- Analysing RPE and well-being data
- Monitoring training load
- Analysing performance testing data
- Online quiz
- Athlete monitoring assignment
Unit 3: Applied Data Analysis in Sport
3.1 Data management and transformation
3.2 Determining associations
3.3 Predicting outcomes
3.4 Determining differences
3.5 Data visualisation
Unit 4: Sport Informatics and Analytics
4.2 Pattern recognition
4.3 Performance monitoring
4.4 Audiences and messages
Formative ePortfolio to document engagement with unit 4.
I am hopeful that many of the resources I have been aggregating and curating will be supportive of Jocelyn’s work, particularly with unit 4 and this WikiEducator resource.
I hope this course is of interest to the sport industry. One of my ideas is that we support people who are in sport by offering flexible and open learning opportunities. I acknowledge too that some people might like a fee-for service structured attention opportunity that aligns them closely with a university and provides blended learning experiences.
I think that Jocelyn’s work can articulate with other institutions and communities of practice as each of decides how we continue to learn.